System and Method for Performing Quality Control of Manufactured Models

ABSTRACT

Disclosed herein are example embodiments of methods and systems for identifying manufacturing defects of a manufactured dentition model. One of the methods for performing quality control comprises: determining whether the manufactured dentition model is a good or a defective product based on a statistical characteristic of a differences model. The differences model can be generated based on differences between a scanned 3D patient-dentition data and a scanned 3D manufactured-dentition data. The scanned 3D patient-dentition data can be generated using 3D data of a patient&#39;s dentition, and the scanned 3D manufactured-dentition data can be generated using 3D data of the manufactured dentition model. The manufactured dentition model can be a 3D printed model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation patent application of U.S. patentapplication Ser. No. 16/656,445, filed Oct. 27, 2019, which is acontinuation-in-part patent application of U.S. patent application Ser.No. 16/457,199, filed Jun. 28, 2019, now U.S. Pat. No. 11,210,788, whichis a continuation-in-part patent application of U.S. patent applicationSer. No. 15/928,484, filed Mar. 22, 2018, now U.S. Pat. No. 11,120,179,the disclosures of each of the foregoing applications are incorporatedherein by reference in its entireties for all purposes.

TECHNICAL FIELD

The disclosure relates generally to the field of quality control,specifically and not by way of limitation, some embodiments are relatedto automatically performing quality control on manufactured dentalprostheses.

BACKGROUND

Recently, CAD/CAM dentistry (Computer-Aided Design and Computer-AidedManufacturing in dentistry) has provided a broad range of dentalrestorations, including crowns, veneers, inlays and onlays, fixedbridges, dental implant restorations, orthodontic appliances, anddentition models of a patient's dentition. In a typical CAD/CAM baseddental procedure, a treating dentist can prepare the tooth beingrestored either as a crown, inlay, onlay, or veneer. The prepared toothand its surroundings are then scanned by a three-dimensional (3D)imaging camera and uploaded to a computer for design. Alternatively, adentist can obtain an impression of the tooth to be restored and theimpression may be scanned directly, or formed into a model to bescanned, and uploaded to a computer for design.

Dental prostheses are typically manufactured at specialized dentallaboratories that employ computer-aided design (CAD) and computer-aidedmanufacturing (CAM) milling systems to produce dental prosthesesaccording to patient-specific specifications provided by dentists. In atypical workflow, information about the oral situation of a patient isreceived from a dentist, and the dentist or dental laboratory can designthe dental prosthesis, which is milled from a block of material.

After the milling process, the milled material blocks are cleaned. Next,the blocks are manually inspected prior to being transferred to asintering tray for the sintering process. Once the dental prosthesis issintered, glazed, cleaned, and inspected, it can be delivered to thedentist.

Today, most dentists expect the newly manufactured dental prosthesis tobe shipped with a dentition model of the patient for which the dentalprosthesis was designed. This enables the dentist to test how the dentalprosthesis would look and fit on the manufactured (e.g., tangible)dentition model that was generated using the patient's 3D dentitiondata. Some dentists even come to rely on the manufactured dentitionmodel as the final quality checking process. For example, some dentistswould send back the dental prosthesis if it does not fit well with themanufactured dentition model. Accordingly, designing and manufacturingan accurate manufactured dentition model is important to prevent falsedefect rejections of a dental prosthesis.

SUMMARY

Disclosed are example embodiments of methods and systems for identifyingand quantifying manufacturing defects of a manufactured dentition model.One of the methods for performing quality control comprises: determiningwhether the manufactured dentition model is a good or a defectiveproduct based on a statistical characteristic of a differences model.

The differences model can be generated based on differences between ascanned 3D patient-dentition data and a scanned 3Dmanufactured-dentition data. The scanned 3D patient-dentition data canbe generated using 3D data of a patient's dentition, and the scanned 3Dmanufactured-dentition data can be generated using 3D data of themanufactured dentition model. The manufactured dentition model can be a3D printed model.

In some embodiments, the differences model can be generated using onlydata corresponding to a tooth. That is data from both the scanned 3Dpatient-dentition data and the scanned 3D manufactured-dentition datathat do not correspond to a tooth or tooth site are excluded. Thedifferences model can also exclude data that correspond to an artificialdentition fixture, which is a structure of the manufactured dentitionmodel that is not associated with teeth and gum structures. Theartificial dentition fixture of a manufactured dentition model caninclude a base and a hinge. The base supports the hinge, one or moretooth structures, and gum structures. The hinge is configured to bepivotably attached to an opposing manufactured dentition model that isconfigured to simulate a bite when mated with the manufactured dentitionmodel.

The manufactured dentition model can have a recess between a pair oftooth structures. The recess can be introduced by modifying the data ofthe scanned 3D patient-dentition data. Using a 3D modeling software, thescanned 3D patient-dentition data can be viewed and edited in 3D. Here,one or more slots (e.g., ditch) can be added at the gum area between twoteeth or tooth structures. The slot can extend beyond from where the gumline starts to a predetermined depth, which can have a range between 5%to 75% of a total thickness of gum (at the same location). In someembodiments, the predetermined depth has a range of 45% of the totalthickness of the gum between the pair of tooth structures.

In some embodiments, the manufactured dentition model is considered tobe a good part when all offsets within ±50 microns account for greaterthan 85% of all points in the differences model. Similarly, themanufactured dentition model is considered to be a good part when alloffsets within ±50 microns account for greater than 85% of all points inthe differences model and all offsets greater than ±75 microns accountfor less than 1% of all points in the differences model. Themanufactured dentition model is considered to be too small or too largewhen a distribution of differences is negatively or positively biased ascompared to a normal distribution.

One of the systems for performing quality control on a manufactureddentition model comprises: a quality control module configured todetermine whether the manufactured dentition model is a good or adefective product based at least on a statistical characteristic of adifferences model, wherein the differences model comprises differencesbetween a scanned 3D patient-dentition data and a scanned 3Dmanufactured-dentition data. The scanned 3D patient-dentition datacomprises 3D data of a patient's dentition, and the scanned 3Dmanufactured-dentition data comprises 3D data of the manufactureddentition model.

In yet another embodiment, a second method for performing qualitycontrol on a 3D-printed dentition model comprises: obtaining a scanned3D dentition data of dentition of a patient; printing the 3D-printeddentition model using the scanned 3D dentition data; generating adifferences model by comparing the scanned 3D dentition data of thepatient and a scanned 3D manufactured-dentition data generated byscanning the manufactured dentition model; and determining whether the3D-printed dentition model is a good or a defective product based on astatistical characteristic of a differences model.

The features and advantages described in the specification are not allinclusive and, in particular, many additional features and advantageswill be apparent to one of ordinary skill in the art in view of thedrawings, specification, and claims. Moreover, it should be noted thatthe language used in the specification has been principally selected forreadability and instructional purposes and may not have been selected todelineate or circumscribe the disclosed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The details of the subject matter set forth herein, both as to itsstructure and operation, may be apparent by study of the accompanyingfigures, in which like reference numerals refer to like parts. Thecomponents in the figures are not necessarily to scale, emphasis insteadbeing placed upon illustrating the principles of the subject matter.Moreover, all illustrations are intended to convey concepts, whererelative sizes, shapes and other detailed attributes may be illustratedschematically rather than literally or precisely.

FIG. 1 is a high-level block diagram of manufacturing and qualitycontrol system in accordance with some embodiments of the presentdisclosure.

FIGS. 2A, 2B, and 2C are various perspective views of a manufactureddentition model.

FIG. 3 illustrates a manufactured dentition model in accordance withsome embodiments of the present disclosure.

FIG. 4 illustrates a differences model of a bad part in accordance withsome embodiments of the present disclosure.

FIG. 5 illustrates a differences model of a good part in accordance withsome embodiments of the present disclosure.

FIG. 6 is a flow chart of a QC process in accordance with someembodiments of the present disclosure.

FIG. 7 is a system diagram that can be used to implement the system andmethod for performing quality control in accordance with someembodiments of the present disclosure.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerousspecific details are set forth to provide a thorough understanding ofthe invention. However, it will be apparent to one skilled in the artthat the invention can be practiced without these specific details. Inother instances, structures and devices are shown in a block diagramform in order to avoid obscuring the invention.

Overview

A patient's dentition data set can include one or more of the patient'sscan data from multiple and/or duplicative scans of various portions ofthe patient's mouth. For example, each scan data can be a scan of one ormore portions of the patient's jaw. The manufactured dentition modeldescribed herein can be fabricated using 3D data (e.g., electronicimage) of at least a portion of a patient's dentition. A patient'sscanned dentition data (also referred to as scanned 3D patient-dentitiondata) can be obtained by a direct intraoral scan of the patient's teeth.It could also be obtained indirectly in several ways, such as, byscanning an impression of the patient's teeth, by scanning a physicalmodel of the patient's teeth, or by other methods known to those skilledin the art. Using the scanned 3D patient-dentition data, acomputer-implemented model manufacturing system can create a physicalmodel of the patient's dentition, which can be used by dentists to testthe “look” and “feel” of a dental prosthesis.

To generate the scanned 3D patient-dentition data, one or more scans canbe performed on the patient's teeth, depending upon where the dentalprosthesis is to be installed. For example, an occlusal, a lingual, anda buccal scan can be performed on both the preparation and the opposingjaws. For example, a single scan with the jaws in occlusion can be takenfrom the buccal perspective to establish the proper occlusionrelationship between the preparation jaw and the opposing jaw.Additionally, in some embodiments, interproximal scans are added tocapture the contact areas of neighboring teeth. Once the scanningprocess is completed, a scanning system (not shown) will assemble theplurality of scans into a merged digital model—forming the scanned 3Dpatient-dentition data.

The scanned patient-dentition data can then be used to manufacturedentition model using a lathing and milling system or a 3D printingsystem. In some embodiments, where a 3D printer is used to create themanufactured dentition model, the thickness of each layer beingdeposited can range between 25-125 microns. For example, the layerthickness can also be between 45-80 microns. In some embodiments, thelayer thickness is 50 microns. The materials used by the 3D printer tocreate the manufactured dentition model can be plastics (e.g., ABSplastic, polyurethane, acrylic esters) nylon, metals, or metal alloys.In some embodiments, medical grade thermal plastics are used becausethey are easy to handle, and the printing time is faster than that ofmetal. In some embodiments, the 3D printing material used is a medicalgrade acrylic esters.

As alluded to above, once the scanned patient-dentition data isobtained, it is used to fabricate a 3D printed model (i.e., themanufactured dentition model) that will be eventually shipped to thedentist. Once printed, the manufactured dentition model can be curedusing a curing oven to permanently set the material or to obtaintemperature stability.

The curing process is carefully controlled. Each curing oven can havedifferent curing profiles for different materials. For example, athermal plastic from company A can have a different curing profile thanthe curing profile of a thermal plastic from company B. The curingprofile for each type of curing oven and material can be developedthrough experimentations.

After the manufactured dentition model is cured, it can be scanned usinga 3D scanner that is specifically set up to scan the manufactureddentition model. In some embodiments, the manufactured dentition modelis modified to include features in the scanned patient-dentition dataand additional dentition fixtures such as, but not limited to, a baseand a hinge. The base can have different shapes such as a square or arectangle. The base provides a foundation on which dentition featuresfrom the scanned patient-dentition data are manufactured. Examplesdentition features are teeth, crown preparation site (e.g., a preptooth), gum line, individual tooth surface anatomy and size, gumfeatures, and other dental features. Examples of tooth surface anatomyfeatures are buccal and lingual cusps, occlusal surface, and buccal andlingual arcs.

The base also provides the foundation for the hinge to be printed. Thehinge can be used to pivotably couple an opposing manufactured dentitionmodel to simulate bites of both the upper and lower jaw. However, duringthe quality control process, the hinge and the foundation are excludedfrom the quality analysis because they are structures unrelated to theperformance and quality of a dental prosthesis.

Next, the manufactured dentition model is scanned to generate anotherset of 3D data—the scanned 3D manufactured-dentition data. This datasetand the scanned 3D patient-dentition data are then used to generate adifferences model upon which quality control is performed. Sincedentists commonly rely on the manufactured dentition model to test thequality of a dental prosthesis, it becomes important to provide dentistswith a highly accurate manufactured dentition model.

Quality Control System

FIG. 1 illustrates a dentition model manufacturing and QC system 100 inaccordance with some embodiments of the present disclosure. System 100includes a dentition scanner 105, a fabrication module 110, a curingoven 115, a model scanner 120, and a QC module 125. Dentition scanner105 can be an intraoral scanner that scan a patient's dentitiondirectly. Alternatively, dentition scanner 105 can be an impressionscanner. In this scenario, an impression of the patient's dentition canbe made using an impression material. The impression can then be scannedwith dentition scanner 105. One or more impressions can be taken, andone or more scans can be performed on each impression. Using abest-fitting algorithm, data from one or more scans can be combined tocreate a merged patient's dentition data set.

Fabrication module 110 can fabricate a manufactured dentition modelusing the data from dentition scanner 105. Fabrication module 110 can bean injection molding system, a lathing and milling system, or a 3Dprinting system. In some embodiments, fabrication module 110 is a 3Dprinting system. For example, fabrication module 110 can be a 3Dprinting system such as a stereolithography (SLA) 3D printer or adigital light processing (DLP) 3D printer. The 3D printing material usedcan be a photopolymer, methacrylate based polymer, ester based polymer,ABS plastic, thermal plastic, acrylic esters, or a medical gradeplastic.

Fabrication module 110 can be configured to deposit a layer of materialhaving a thickness range from 25 microns to 125 microns. A low thicknesslayer such as 20 microns would yield very detailed and accurate in themanufactured dentition model. However, the time it would take tocomplete the printing process might be too long for full production. Incontrast, a high layer thickness such as 150 microns would yield a lessaccurate dentition model, but it would be much faster to produce. Thelayer thickness can depend on the material and the type of 3D printerused. In some embodiments, the optimum layer thickness has a range from45 to 65 microns. FIGS. 2A-C will now be discussed in concurrent withFIG. 1

FIGS. 2A-C illustrate a manufactured dentition model 200 in accordancewith some embodiments of the present disclosure. Dentition model 200includes a base 205, a hinge 210, and dentition features area 215. Base205 provides a foundation for dentition features area 215 to befabricated. Base 205 also supports hinge 210, which is configured to bepivotably attached to a corresponding opposing dentition model. Forexample, dentition model 200 can be a portion of the lower jaw.Accordingly, the corresponding opposing dentition model is the portionof the upper jaw that forms a bite with dentition model 200. Dentitionfeatures area 215 can include one or more teeth, one or more prep sites,and gums. Dentition model 200 can also include identification area 220that contain identifying information of the dentition model and/or thedental prosthesis for which dentition model 200 was made. An identifyinginformation can also be created on side wall 225 of dentition model 200.In some embodiment, the identifying information can be a barcode, QRcode, or numbers.

FIG. 3 illustrates a manufactured dentition model 300 fabricated inaccordance with some embodiments of the present disclosure. Dentitionmodel 300 is similar to dentition model 200 and can include one or morefeatures of dentition model 200 as described above. The main distinctionof dentition model 300 is the recesses (e.g., ditch) formed between twoteeth or tooth structures. As shown, recess 305 is between tooth 310 andprepped tooth 315. Recess 320 is formed between prepped tooth 315 andtooth 325. Both recesses are artificial—not a natural dentition feature.In some embodiments, recesses 305 and 320 can be created by modifyingthe scanned 3D patient-dentition data so that the recesses are createdduring the printing process. Recesses 305 and 320 can also be createdafter the 3D printing process using post processing procedures such asmilling.

Recesses 305 and 320 are considered to be a full recess—the depth of therecess is substantially equal to the gum thickness at that location. Asshown in FIG. 3, recesses 305 and 320 almost reach the base of dentitionmodel 300. In some embodiments, recesses 305 and 320 are partialrecesses, which means the depth of each recess is a fraction of the gumthickness. For example, the depth of recess 305 can be half of the totalgum thickness 330. In another example, the depth of recess 305 can be45% of the total gum thickness. Similarly, the depth of recess 320 canbe 35-75% of thickness 335. In yet another example, the depth ofrecesses 305 and/or 320 can range from 0.05-1 mm. For instance, thedepth of recesses 305 and/or 320 can be 0.1 mm. A partial recessed modelcan provide sufficient relief of the soft tissue (e.g., gum) in the scanso that a dental prosthesis can sit on the margin and be visible forinspection. A fully recessed model can introduce warpage to the modelonce it is cured. Accordingly, a partial recessed model provides thebenefits of both the non-recessed and full-recessed model. Partiallyrecessed models can be advantageous because they easier to print whilestill maintaining structural integrity of a non-recessed model (nowarpage) while providing sufficient room for a dental prosthesis to bemounted for testing.

Certain 3D printing material needs to be cured before the materialbecome stable and can be handled without causing damage to the part.Curing oven 115 can have a predetermined curing profile based at leaston the type of 3D printer and the type of 3D printing material used tofabricate dentition model 200. For example, the temperature ramp rateand curing time can vary depending upon the material used. In anotherexample, the temperature ramp rate and hold time can fluctuate andchange every number of minutes or hours.

Once dentition model 200 is cured, it can be scanned to create a 3D dataof dentition model 200. Model scanner 120 can be a contact ornon-contact inspection device that can generate a scanned data set ofthe manufactured dentition model. The scanned data model can be a 3Dmodel or 2D calibrated model. Scanner 120 can use light or radio wavesto scan the manufactured dentition model. Scanner 120 can generate a 3Ddata set of the scanned dental prosthesis in a stereolithography CADformat known as STL. Scanner 120 can also generate other types of 3Ddata set format such as 3DS, BLEN, SCL, and SKP. In some embodiments,scanner 120 can scan a dental prosthesis and generate a 2D calibratedimage. To generate the 3D data set, scanner 110 can use 3D scanningtechnology such as, but not limited to, laser triangulation, structuredlight, laser profilometer, focus variation, OCT, conoscopic hologaphy,confocal microscopy, contact measurement, and photogrammetry.

Model scanner 120 can include a holder (not shown) that can be rotated360 degrees and can be translated in one or more directions (e.g., x, y,and z directions). The holder is configured to hold and secure themanufactured dentition model (e.g., dentition model 200) while it isbeing scanned. The holder also includes a controller that can rotate andmove the manufactured dentition model (while it is being held by theholder) based on a scanning profile, which specifies the rate anddirection of rotations, the rate and direction of translations, the tiltangle, the number of cycles, and the hold rate after each rotation,translation, and/or tilt motion. The tilt angle is an angle in which theholder tilts the manufactured dentition model. A tilt angle of 0° meansthere is zero tilt and that the dentition model is parallel with respectto the ground. A tilt angle of 90° would cause the dentition model to beapproximately perpendicular to the ground.

When scanning, model scanner 120 can rotate, tilt and/or translate themanufactured dentition model such that sharp edges on the manufactureddentition model are not perpendicular to the incident light. In otherwords, the manufactured dentition model is moved (a combination ofrotation, translation and titling) such that sharp edges are not at 90°degrees with respect to the scanner's light source. For example(referring to FIG. 2A), model scanner 120 can scan dentition model 200at many directions and angles except starting from hinge side(posterior) 235, base side 240, or anterior side 245. For other areas ofthe manufactured dentition model, model scanner 120 is configured torotate, tilt and/or translate the manufactured dentition model such thatmost (substantially all) of the dentition features of are perpendicularto the incident light.

TABLE 1 Rotation Scanning Sequence Angle Tilt Angle portion 1 0 90Occlusal 2 60 90 3 120 90 4 180 90 5 240 90 6 300 90 7 300 0 Periphery 8240 0 9 180 0 10 120 0 11 60 0 12 0 0 13 45 0 Detail scan of 14 45 20Crevices 15 45 40 16 45 60 17 45 80 18 135 80 19 135 60 20 135 40 21 13520 22 135 0 23 225 0 24 225 20 25 225 40 26 225 60 27 225 80 28 315 8029 315 60 30 315 40 31 315 20 32 315 0

In some embodiments, model scanner 120 can have a scanning profile asspecified in Table 1. For each rotation and tilt angle setting, modelscanner 120 can scan the manufactured dentition model by moving (e.g.,translating, rotating) the scanning head and/or light source(s) or bytranslating the holder. Each row of Table 1 can represent a scanningsequence. Model scanner 120 can perform one or more sequences ofscanning for each row. After a sequence is completed, the next setting(next row) is performed until the entire scanning cycle (all rows) iscompleted.

Each scanning sequence can scan one or more portions of the manufactureddentition model. For example, the occlusal and/or periphery portions ofthe manufactured dentition model can be scanned during one or moresequences. In some embodiments, only one portion of the manufactureddentition model is scanned during a sequence. Alternatively, one portionof the manufactured dentition model can be repeatedly scanned overmultiple sequences. For example, as shown in Table 1, the occlusalportion of the manufactured dentition model can be scanned in sequences1 through 6. The periphery portion of the manufactured dentition modelcan be scanned in sequences 7 through 12. Finally, the crevices portionsof the manufactured dentition model can be scanned in sequences 13through 32.

QC module 125 can include a data preprocessing module (not shown)configured to pre-process each 3D data set. There are two 3D data setsthat are used to generate the differences model, on which qualitycontrol will be based. The two 3D data sets are the scanned 3Dpatient-dentition data and the scanned 3D manufactured-dentition data.Data preprocessing can be necessary to trim unwanted data that couldotherwise inject errors into the differences model. For example, datapoints corresponding to the roof of the mouth or untreated area of thejaw can be removed from the scanned 3D patient-dentition data. Datapoints corresponding to the base, the hinge, and recesses in the scanned3D manufactured-dentition data can also be omitted. If these data fromthese regions are not omitted, they could negatively affect thebest-fitting algorithm and thereby lead to false negative errors. Forexample, in generating the differences model, data points from recesses305 and 320 can be omitted.

In some embodiments, data for the differences model only include datapoints that correspond to a tooth or a prepped site. For example, onlydata relating to tooth 310, 315, and 325 will be used to generate thedifferences model. Any data relating to recesses 305 and 320, the base,or the hinge can be omitted.

The data preprocessing module can be an integrated component of QCmodule 125 or it can be an independent module that can be called (via anapplication programming interface). The data preprocessing module caninclude dentition modeling software and graphical user interfaces (GUI)that enable a user to select and modify various locations on thedifferences model.

QC module 125 can determine whether the manufactured dentition model isa good or defective part based on the distribution of offsets of thedifferences model, which can comprise of data points of offset values.An offset is determined by the difference in distance between a point inthe scanned 3D patient-dentition data and the corresponding best-fittingpoint in the scanned 3D manufactured-dentition data. For a perfectmatch, the offset value is zero.

For example, in a good part, the distribution of all offset values ofthe difference model is similar to a normal distribution. In a defectivepart, the distribution of the offsets is biased in the negative orpositive direction, or it could have two or more peaks. A negativedirection is toward the negative side from the center of the normaldistribution. Alternatively, a defective part can have a distributionwith one or more peaks in the negative or positive direction. In thisway, QC module 125 can determine whether the manufactured dentitionmodel is good or bad.

In some embodiments, QC module 125 can determine whether themanufactured dentition model is a good or defective part based on thedistribution of offsets of the differences model, which can comprise ofdata points of offset values. QC module 125 can also provide feedback tothe 3D printer and/or the curing oven, based at least on the differencesmodel, so that various manufacturing processes (e.g., layer thickness,temperature profile) of the 3D printer and/or the curing oven can beadjusted. In this way, a feedback loop can be established so thatsubsequent productions of the dentition model can have better quality.

FIG. 4 illustrates a differences model 400 of a bad manufactureddentition model. In some embodiments, QC module 125 can flag amanufactured dentition model as a bad part when over 10% of the offsetsare over ±75 microns. In some embodiments, a part can be considered badwhen over 30% of the offsets are over ±75 microns. In some embodiments,only offsets of data corresponding to an area of a tooth or prepped siteare included in the statistical analysis. For example, datacorresponding to gum areas below the gumline are excluded.Alternatively, all data points except data points corresponding to thebase and the hinge can be included in the statistical analysis.

Alternatively, QC module 125 can flag a manufactured dentition model asa bad part when over 15% of the offsets are over ±50 microns. Inversely,QC module 125 can flag a manufactured dentition model as a good partwhen over 85% of the offsets are within ±50 microns. In one embodiment,QC module 125 can flag a manufactured dentition model as a good partwhen over 85% of the offsets are within ±75 microns.

FIG. 5 illustrates a differences model 500 of a good manufactureddentition model. QC module 125 can flag a manufactured dentition modelas a good part when all offsets within ±50 microns account for greaterthan 85% of all points in the differences model and all offsets greaterthan ±75 microns account for less than 1% of all points in thedifferences model.

FIG. 6 is a flow chart of an auto QC process 600 in accordance with someembodiments of the present disclosure. Process 600 starts at 605 where ascan of a patient's dentition is made using an intraoral 3D scanner.Alternatively, an impression of the patient's dentition can be made andthen the impression can be scanned to generate a 3D dentition data set.In some embodiments, modification to the scanned 3D patient-dentitiondata can be made. For example, the base, hinge and recesses could beadded to the data set. Additionally, extraneous data such as unwantedportion of the jaw (e.g., extra teeth data, tongue, roof of the mouth)that were included in the scan can be deleted.

At 610, using a 3D printer, a manufactured dentition model can befabricated using the scanned 3D patient-dentition data or the modifiedscanned 3D patient-dentition data (e.g., modified dataset to include thebase, hinge, and one or more recesses). At 615, the manufactureddentition model is scanned to generate the scanned 3Dmanufactured-dentition data.

At 620, a best-fitting algorithm is performed on the scanned 3Dpatient-dentition data and the scanned 3D manufactured-dentition data togenerate a differences model. Each data point in the differences modelcan have an offset value. An offset value of zero means the data pointsin both data set matches perfectly. If the match is not perfect, anoffset value will be registered. For example, if an offset between twocorresponding points is 50 microns, then the offset value for the datapoint of the differences model is 50 microns.

At 625, a manufactured dentition model can be identified as a good orbad part based at least on a statistical analysis of the differencesmodel. For example, if the distribution has only one peak and is anormal distribution, and the standard deviation is below a certainthreshold, then the part can be considered to be good. Inversely, if thestandard deviation of all the data points exceed a certain value, thenthe part can be considered to be bad.

FIG. 7 illustrates an overall system or apparatus 700 in which system100 and process 600 can be implemented. In accordance with variousaspects of the disclosure, an element, or any portion of an element, orany combination of elements may be implemented with a processing system714 that includes one or more processing circuits 704. Processingcircuits 704 may include micro-processing circuits, microcontrollers,digital signal processing circuits (DSPs), field programmable gatearrays (FPGAs), programmable logic devices (PLDs), state machines, gatedlogic, discrete hardware circuits, and other suitable hardwareconfigured to perform the various functionality described throughoutthis disclosure. That is, the processing circuit 704 may be used toimplement any one or more of the processes described above andillustrated in FIGS. 4 through 12.

In the example of FIG. 7, the processing system 714 may be implementedwith a bus architecture, represented generally by the bus 702. The bus702 may include any number of interconnecting buses and bridgesdepending on the specific application of the processing system 714 andthe overall design constraints. The bus 702 links various circuitsincluding one or more processing circuits (represented generally by theprocessing circuit 704), the storage device 705, and a machine-readable,processor-readable, processing circuit-readable or computer-readablemedia (represented generally by a non-transitory machine-readable medium706.) The bus 702 may also link various other circuits such as timingsources, peripherals, voltage regulators, and power management circuits,which are well known in the art, and therefore, will not be describedany further. The bus interface 707 provides an interface between bus 702and a transceiver 710. The transceiver 710 provides a means forcommunicating with various other apparatus over a transmission medium.Depending upon the nature of the apparatus, a user interface 712 (e.g.,keypad, display, speaker, microphone, touchscreen, motion sensor) mayalso be provided.

The processing circuit 704 is responsible for managing the bus 702 andfor general processing, including the execution of software stored onthe machine-readable medium 706. The software, when executed byprocessing circuit 704, causes processing system 714 to perform thevarious functions described herein for any particular apparatus.Machine-readable medium 706 may also be used for storing data that ismanipulated by processing circuit 704 when executing software.

One or more processing circuits 704 in the processing system may executesoftware or software components. Software shall be construed broadly tomean instructions, instruction sets, code, code segments, program code,programs, subprograms, software modules, applications, softwareapplications, software packages, routines, subroutines, objects,executables, threads of execution, procedures, functions, etc., whetherreferred to as software, firmware, middleware, microcode, hardwaredescription language, or otherwise. A processing circuit may perform thetasks. A code segment may represent a procedure, a function, asubprogram, a program, a routine, a subroutine, a module, a softwarepackage, a class, or any combination of instructions, data structures,or program statements. A code segment may be coupled to another codesegment or a hardware circuit by passing and/or receiving information,data, arguments, parameters, or memory or storage contents. Information,arguments, parameters, data, etc. may be passed, forwarded, ortransmitted via any suitable means including memory sharing, messagepassing, token passing, network transmission, etc.

The software may reside on machine-readable medium 706. Themachine-readable medium 706 may be a non-transitory machine-readablemedium. A non-transitory processing circuit-readable, machine-readableor computer-readable medium includes, by way of example, a magneticstorage device (e.g., hard disk, floppy disk, magnetic strip), anoptical disk (e.g., a compact disc (CD) or a digital versatile disc(DVD)), a smart card, a flash memory device (e.g., a card, a stick, or akey drive), RAM, ROM, a programmable ROM (PROM), an erasable PROM(EPROM), an electrically erasable PROM (EEPROM), a register, a removabledisk, a hard disk, a CD-ROM and any other suitable medium for storingsoftware and/or instructions that may be accessed and read by a machineor computer. The terms “machine-readable medium”, “computer-readablemedium”, “processing circuit-readable medium” and/or “processor-readablemedium” may include, but are not limited to, non-transitory media suchas portable or fixed storage devices, optical storage devices, andvarious other media capable of storing, containing or carryinginstruction(s) and/or data. Thus, the various methods described hereinmay be fully or partially implemented by instructions and/or data thatmay be stored in a “machine-readable medium,” “computer-readablemedium,” “processing circuit-readable medium” and/or “processor-readablemedium” and executed by one or more processing circuits, machines and/ordevices. The machine-readable medium may also include, by way ofexample, a carrier wave, a transmission line, and any other suitablemedium for transmitting software and/or instructions that may beaccessed and read by a computer.

The machine-readable medium 706 may reside in the processing system 714,external to the processing system 714, or distributed across multipleentities including the processing system 714. The machine-readablemedium 706 may be embodied in a computer program product. By way ofexample, a computer program product may include a machine-readablemedium in packaging materials. Those skilled in the art will recognizehow best to implement the described functionality presented throughoutthis disclosure depending on the particular application and the overalldesign constraints imposed on the overall system.

One or more of the components, steps, features, and/or functionsillustrated in the figures may be rearranged and/or combined into asingle component, block, feature or function or embodied in severalcomponents, steps, or functions. Additional elements, components, steps,and/or functions may also be added without departing from thedisclosure. The apparatus, devices, and/or components illustrated in theFigures may be configured to perform one or more of the methods,features, or steps described in the Figures. The algorithms describedherein may also be efficiently implemented in software and/or embeddedin hardware.

Reference in the specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin connection with the embodiment is included in at least one embodimentof the invention. The appearances of the phrase “in one embodiment” invarious places in the specification are not necessarily all referring tothe same embodiment.

Some portions of the following detailed description are presented interms of algorithms and symbolic representations of operations on databits within a computer memory. These algorithmic descriptions andrepresentations are the methods used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared or otherwise manipulated. It has provenconvenient at times, principally for reasons of common usage, to referto these signals as bits, values, elements, symbols, characters, terms,numbers or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following disclosure,it is appreciated that throughout the disclosure terms such as“processing,” “computing,” “calculating,” “determining,” “displaying” orthe like, refer to the action and processes of a computer system, orsimilar electronic computing device, that manipulates and transformsdata represented as physical (electronic) quantities within the computersystem's registers and memories into other data similarly represented asphysical quantities within the computer system's memories or registersor other such information storage, transmission or display.

Finally, the algorithms and displays presented herein are not inherentlyrelated to any particular computer or other apparatus. Variousgeneral-purpose systems may be used with programs in accordance with theteachings herein, or it may prove convenient to construct morespecialized apparatus to perform the required method steps. The requiredstructure for a variety of these systems will appear from thedescription below. It will be appreciated that a variety of programminglanguages may be used to implement the teachings of the invention asdescribed herein.

The figures and the following description describe certain embodimentsby way of illustration only. One skilled in the art will readilyrecognize from the following description that alternative embodiments ofthe structures and methods illustrated herein may be employed withoutdeparting from the principles described herein. Reference will now bemade in detail to several embodiments, examples of which are illustratedin the accompanying figures. It is noted that wherever practicablesimilar or like reference numbers may be used in the figures to indicatesimilar or like functionality.

The foregoing description of the embodiments of the present inventionhas been presented for the purposes of illustration and description. Itis not intended to be exhaustive or to limit the present invention tothe precise form disclosed. Many modifications and variations arepossible in light of the above teaching. It is intended that the scopeof the present invention be limited not by this detailed description,but rather by the claims of this application. As will be understood bythose familiar with the art, the present invention may be embodied inother specific forms without departing from the spirit or essentialcharacteristics thereof. Likewise, the particular naming and division ofthe modules, routines, features, attributes, methodologies and otheraspects are not mandatory or significant, and the mechanisms thatimplement the present invention or its features may have differentnames, divisions and/or formats.

Furthermore, as will be apparent to one of ordinary skill in therelevant art, the modules, routines, features, attributes, methodologiesand other aspects of the present invention can be implemented assoftware, hardware, firmware or any combination of the three. Also,wherever a component, an example of which is a module, of the presentinvention is implemented as software, the component can be implementedas a standalone program, as part of a larger program, as a plurality ofseparate programs, as a statically or dynamically linked library, as akernel loadable module, as a device driver, and/or in every and anyother way known now or in the future to those of ordinary skill in theart of computer programming.

Additionally, the present invention is in no way limited toimplementation in any specific programming language, or for any specificoperating system or environment. Accordingly, the disclosure of thepresent invention is intended to be illustrative, but not limiting, ofthe scope of the present invention, which is set forth in the followingclaims.

1. A system for performing quality control on a physical dentitionmodel, the system comprising: a fabrication module configured tomanufacture a physical dentition model that is based upon a virtualmodel of a patient's dentition; a dentition model scanner configured toscan the physical dentition model to thereby obtain a scanned model; anda quality control module comprising a processor and a non-transitorycomputer-readable storage medium comprising instructions executable bythe processor to determine whether the manufactured physical dentitionmodel is a good or a defective product based on a statisticalcharacteristic of a differences model generated by comparing spatialpoints of the virtual model of the patient's dentition and correspondingbest-fitting points of the scanned model to compile offsets between thespatial points of the virtual model and the corresponding best-fittingspatial points of the scanned model.
 2. The system of claim 1, whereinthe quality control module is configured to determine whether the dentalprosthesis is a good or defective product by determining a standarddeviation of a difference value of points in the differences model. 3.The system of claim 1, wherein the quality control module is configuredto generate the differences model using only data corresponding to atooth.
 4. The system of claim 1, wherein the quality control module isconfigured to generate the differences model using data corresponding toa dentition fixture, wherein the dentition fixture is a structure of themanufactured physical dentition model that is not associated with teethand gum structures.
 5. The system of claim 1, wherein the fabricationmodule comprises a 3D printing module configured to print the physicaldentition model having a base, a hinge, and dentition structures,wherein the dentition structures comprise one or more teeth and gumstructures, wherein the base is configured to support the dentitionstructures, and the hinge is configured to be pivotably attached to anopposing physical dentition model that when mated with the physicaldentition model simulates a bite.
 6. The system of claim 1, wherein thefabrication module comprises an injection molding system.
 7. The systemof claim 1, wherein the fabrication module comprises a mill.
 8. Thesystem of claim 1, wherein the physical dentition model comprises arecess between a pair of tooth structures, wherein the recess is a slotin the physical dentition model that extends beyond where a gum linestarts by a predetermined depth.
 9. The system of claim 8, wherein thepredetermined depth has a range between 5% to 75% of a total thicknessof gum between the pair of tooth structures.
 10. The system of claim 8,wherein the predetermined depth has a range of 45% of the totalthickness of the gum between the pair of tooth structures.
 11. Thesystem of claim 1, wherein the quality control module is configured todetermine whether the physical dentition model is a good or a defectiveproduct further comprises determining the physical dentition model is agood product when all offsets within ±50 microns account for greaterthan 85% of all points in the differences model.
 12. The system of claim1, wherein the quality control module is configured to determine whetherthe physical dentition model is a good or a defective product furthercomprises determining the physical dentition model is a good productwhen all offsets greater than ±75 microns account for less than 1% ofall points in the differences model.
 13. The system of claim 1, whereinthe quality control module is configured to determine whether thephysical dentition model is a good or a defective product furthercomprises determining that the physical dentition model is too small ortoo large based on a distribution of differences that is negatively orpositively biased as compared to a normal distribution.
 14. The systemof claim 1, wherein the physical dentition model comprises a 3D printedmodel.